项目管理中人工智能和机器学习的荟萃分析:优化生物技术中新出现的病毒威胁的疫苗开发。

IF 3.7 2区 医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Medical Informatics Pub Date : 2024-12-18 DOI:10.1016/j.ijmedinf.2024.105768
Jatin Vaghasiya , Mahim Khan , Tarak Milan Bakhda
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引用次数: 0

摘要

目标:人工智能(AI)和机器学习(ML)已经成为各个行业的变革性技术,包括医疗保健、生物技术和疫苗开发。这些技术为提高项目管理效率、决策和资源利用提供了巨大的潜力,特别是在疫苗开发和卫生保健创新等复杂任务中。方法:对截至2024年9月PubMed、IEEE explore、Scopus、Web of Science、EMBASE、谷歌Scholar等数据库的研究进行系统荟萃分析。该分析侧重于使用PICO框架指导研究选择和纳入的人工智能和机器学习在疫苗开发、生物技术和更广泛的医疗保健创新项目管理中的应用。采用Review Manager 5.4和综合meta分析(CMA)软件进行统计分析。结果:荟萃分析回顾了44项研究,研究了人工智能(AI)和机器学习(ML)在医疗保健、生物技术和疫苗开发项目管理中的整合。结果显示在效率、资源分配、决策和风险管理方面有显著的改善。人工智能/机器学习应用显著加速了疫苗开发,从候选疫苗识别到临床试验优化,并改进了有效性和安全性的预测模型。亚组分析揭示了各个医疗保健部门的有效性差异,在传染病控制中观察到的综合效应量最高(1.2;95% CI: 0.85-1.50)与医学影像学(0.85;95% ci: 0.75-0.95)。采用人工智能技术的研究显示,合并效应值为0.83 (95% CI: 0.78-1.08)。尽管观察到高异质性(I2 = 99.04%)和中高偏倚风险,敏感性分析证实了研究结果的稳健性。总体而言,AI/ML集成提供了变革性潜力,可以加强项目管理和疫苗开发,推动这些关键领域的创新和效率。结论:人工智能和机器学习技术通过提高效率、预测分析和决策能力,显示出巨大的潜力,可以改变医疗保健、生物技术和疫苗开发领域的项目管理实践。它们的整合为更多创新的、数据驱动的解决方案铺平了道路,这些解决方案可以适应这些领域不断变化的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A meta-analysis of AI and machine learning in project management: Optimizing vaccine development for emerging viral threats in biotechnology

Objectives

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies across various industries, including healthcare, biotechnology, and vaccine development. These technologies offer immense potential to improve project management efficiency, decision-making, and resource utilization, especially in complex tasks such as vaccine development and healthcare innovations.

Methods

A systematic meta-analysis was conducted by reviewing studies from databases like PubMed, IEEE Xplore, Scopus, Web of Science, EMBASE, and Google Scholar until September 2024. The analysis focused on the application of AI and ML in project management for vaccine development, biotechnology, and broader healthcare innovations using the PICO framework to guide study selection and inclusion. Statistical analyses were performed using Review Manager 5.4 and Comprehensive Meta-Analysis (CMA) software.

Results

The meta-analysis reviewed 44 studies examining the integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, biotechnology, and vaccine development project management. Results demonstrated significant improvements in efficiency, resource allocation, decision-making, and risk management. AI/ML applications notably accelerated vaccine development, from candidate identification to clinical trial optimization, and improved predictive modeling for efficacy and safety. Subgroup analysis revealed variations in effectiveness across healthcare sectors, with the highest pooled effect sizes observed in infectious disease control (1.2; 95 % CI: 0.85–1.50) compared to medical imaging (0.85; 95 % CI: 0.75–0.95). Studies employing AI techniques demonstrated a pooled effect size of 0.83 (95 % CI: 0.78–1.08). Despite the observed high heterogeneity (I2 = 99.04 %) and moderate-to-high risks of bias, sensitivity analyses confirmed the robustness of the findings. Overall, AI/ML integration offers transformative potential to enhance project management and vaccine development, driving innovation and efficiency in these critical fields.

Conclusion

AI and ML technologies show significant potential to transform project management practices in healthcare, biotechnology, and vaccine development by enhancing efficiency, predictive analytics, and decision-making capabilities. Their integration paves the way for more innovative, data-driven solutions that can adapt to evolving challenges in these fields.
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来源期刊
International Journal of Medical Informatics
International Journal of Medical Informatics 医学-计算机:信息系统
CiteScore
8.90
自引率
4.10%
发文量
217
审稿时长
42 days
期刊介绍: International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
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